Indoor Safety of Wireless Power Transfer: A Machine Learning Approach

This article introduces an innovative approach integrating machine learning (ML) methods into a far-field wireless power transfer (WPT) system. Within this system, a radar is employed to detect the presence and height of individuals. These data, combined with information on the location of nodes, as well as the propagation channel between transmitters and nodes, are fed into the proposed ML algorithm. The ML model aims to predict the optimal power level for each transmitter, effectively establishing a safe 3-D zone around people while also maximizing power transfer efficiency. The advantages of using ML are the realization of a real-time system, which is crucial in indoor applications, keeping dangerous radiation at a safe level with a very low risk of harmful exposure, and simultaneously enhancing efficiency. Three ML models are evaluated, namely the random forest (RF), support vector machine (SVM), and neural network (NN). Simulation results highlight the superior performance of the NN model, demonstrating its ability to effectively capture the complex nonlinear characteristics of indoor propagation environments, with only approximately 6% of its predictions exceeding the predefined safety threshold. The experimental results show that NN-based WPT can maintain the electric field amplitude (EFA) below a defined threshold for multiple indoor experimental scenarios over the person’s height. In addition, the proposed approach outperformed the maximum ratio transmission (MRT) approach in terms of radio frequency–radio frequency (RF–RF) transmission efficiency in 21.43% of the measurements conducted with multiple people present in the testbed.